QSimPy: A Learning-centric Simulation Framework for Quantum Cloud Resource Management

• URL: http://arxiv.org/abs/2405.01021v1
• Date: Thu, 2 May 2024 05:41:42 GMT
• Title: QSimPy: A Learning-centric Simulation Framework for Quantum Cloud Resource Management
• Authors: Hoa T. Nguyen, Muhammad Usman, Rajkumar Buyya,
• Abstract summary: We propose QSimPy, a novel discrete-event simulation framework for quantum resource management problems in cloud environments. QSimPy provides a lightweight simulation environment based on SimPy, a well-known Python-based simulation engine. We demonstrate the use of QSimPy in developing reinforcement learning policies for quantum task placement problems.
• Score: 19.006907700170693
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Quantum cloud computing is an emerging computing paradigm that allows seamless access to quantum hardware as cloud-based services. However, effective use of quantum resources is challenging and necessitates robust simulation frameworks for effective resource management design and evaluation. To address this need, we proposed QSimPy, a novel discrete-event simulation framework designed with the main focus of facilitating learning-centric approaches for quantum resource management problems in cloud environments. Underpinned by extensibility, compatibility, and reusability principles, QSimPy provides a lightweight simulation environment based on SimPy, a well-known Python-based simulation engine for modeling dynamics of quantum cloud resources and task operations. We integrate the Gymnasium environment into our framework to support the creation of simulated environments for developing and evaluating reinforcement learning-based techniques for optimizing quantum cloud resource management. The QSimPy framework encapsulates the operational intricacies of quantum cloud environments, supporting research in dynamic task allocation and optimization through DRL approaches. We also demonstrate the use of QSimPy in developing reinforcement learning policies for quantum task placement problems, demonstrating its potential as a useful framework for future quantum cloud research.

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